Information regarding vehicular traffic along highways and at intersections is useful for controlling the flow of traffic, especially during periods of congestion. Present methods of monitoring traffic include, for example, camera-based visual systems and inductive loops buried below the road surface. However, the number of locations that can be monitored in a highway network is limited by the cost and reliability of the monitoring systems. Inductive loops, as an example, are expensive to install, are not always reliable, and must be dug up to be repaired or replaced. Currently used visual systems are expensive because of the large amount of visual data that must be processed digitally for image recognition. Traffic monitoring networks could be made much denser, and thus more effective, if the cost and complexity of reliable monitoring systems could be reduced.
Images collected by sensors, such as imaging focal plane arrays (FPAs), for example, generally contain some points or pixels (which are referred to as "outliers") that are significantly different in brightness or intensity from their surrounding pixels. These points may be the result of glint, for example, or missing data points. Depending on the overall function of the sensor system, outliers may be interest points, such as those produced in the detection of point targets, or noise points, such as those produced by specular reflection from rain drops in laser radar images. A method and apparatus for isolating outliers in visual images is described by Harris et al., "Discarding Outliers Using a Nonlinear Resistive Network," IEEE International Joint Conference on Neural Networks, pp. I-501-506, Jul. 8, 1991.